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Technical skills (e.g., Python, NLP toolkits) are a plus but not required if the candidate brings qualitative or conceptual expertise A combination of education and meaningful experience from which comparable
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application. Proficiency in programming languages such as Python, C, C++, and deep learning libraries like PyTorch or similar. Demonstrated ability to work in a team-oriented software development environment
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have an interest in the use of AI in healthcare. Proven competence in programming methods in Python and similar environments is also essential. Informal enquiries may be addressed to Professor David
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
), especially using Google Earth Engine (preferred). - Proficiency in Python, JavaScript, or R for geospatial analysis and modeling. - Experience handling large-scale Earth observation data, including cloud-based
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date. Proficiency in Python programming and working with large datasets. Demonstrated ability to conduct independent research and collaborate on interdisciplinary teams. Experience in environmental load
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methods in Python and similar environments is also essential. Informal enquiries may be addressed to Professor David Clifton (email: david.clifton@eng.ox.ac.uk). Only online applications received before
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journals and conferences. • Proficiency in computational experiments with programming in Python • Strong writing and presentation skills The postdoctoral researcher will receive guaranteed support from Dr
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demonstrable experience with R and/or with other types of programming (e.g. Python or Matlab). You are a creative and critical researcher and are motivated to pursue an academic career. You are able
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skills Competency in or willingness to learn process-based modelling Strong data management skills and proficiency with analytical tools e.g. Matlab, R, Python Previous experiences with eddy covariance
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer